SENSORY ANALYSIS OF FIBER FORMULATED GROUND PORK PATTIES1

Pure cellulose (Solka-Floc), microcrystalline cellulose (Avicel), and a soluble gum (Nutriloid Fiberplus) were added to ground pork (25%± 2% fat) at 3.5% and 7.0% based on total dietary fiber content of each ingredient. Cooked samples of all treatments and a control were presented to a professional sensory panel and evaluated for the following six attributes: resistance to bite, juiciness, off flavor, pork flavor intensity, graininess/flouriness, and cohesiveness. Products containing Nutriloid Fiberplus at 3.5% and 7.0% had the least resistance to bite (softest), but no differences were detected between the two levels of these products. The Avicel and Solka-Floc products were similar to the control products for resistance to bite. Products containing Nutriloid Fiberplus carried a significant off-flavor, whereas products containing Avicel at 3.5% maintained a flavor similar to that of the control products. Products with Solka-Floc or Nutriloid Fiberplus had distinct graininess/flouriness attributes. Solka-Floc products and Nutriloid products were more cohesive (p < 0.05) than control products. Avicel products at 3.5% were not significantly different than control products for all attributes evaluated.     

SENSORY EVALUATION OF IRRADIATED BEEF AND BACON

Vacuum-packed beef and bacon were treated with gamma- radiation (1–10 KGy) from CO⁶⁰ and stored for 3 months at + 4°C. The samples were evaluated by a trained sensory laboratory panel, directly after irradiation, and after 3 months. Samples given 1.0 KGy and higher doses were found to be significant different from nonirradiated samples at both times of testing. A higher intensity of off-taste, rancidity, metallic taste and sweetness, and a lower intensity of juiciness and color saturation were found in all the irradiated samples. Off-taste and rancidity were found to increase with increasing doses, and were best explained when analyzed by Principal Component Analysis (PCA).     

THE EFFECTS OF COLORED TEXTURED SOYBEAN PROTEIN (TSP) ON SENSORY AND PHYSICAL ATTRIBUTES OF GROUND BEEF PATTIES

Defatted soybean flour mixed with a combination of 0.03% Red No. 40 and 0.01% annatto ( Bixa orellana ) colorants was extruded into textured soy protein (TSP) in a counter-rotating twin extruder to produce a red-meat like product suitable for hamburger patties. Ground beef patties were processed replacing the meat (15 and 30%) with resulting hydrated textured soybean protein (TSP) prepared with and without the colorants. The resulting cooked patties were evaluated by eight trained judges for tenderness, juiciness, number of chews, beef flavor and overall flavor quality using a nine point nonstructured horizontal scale. Other patty characteristics examined included cooking losses (weight loss and diameter reduction) and color, by tristimulus colorimetric measurement. The results from sensory analysis revealed that ground beef patties with high TSP level were more tender than control, and they had less beef flavor and overall flavor quality. Weight loss was not significantly (P > 0.05) affected by TSP level but the patties with 30% TSP showed less shrinkage than the others. Results of the study suggested that while patties with 15% TSP had sensory attributes similar to the control, adding 30% TSP with coloring significantly (P < 0.05) increased the redness of the patties.     

EFFECT OF SAMPLE NUMBERS ON THE SENSORY ACUITY OF A TRAINED GROUND BEEF TEXTURE PANEL

Trained sensory panelists evaluated 8,12 or 18 samples of ground beef at each meeting to determine the effect of sample numbers on panelist perception of certain sensory properties. Six ground beef formulations were selected to represent a broad range of fat and connective tissue content. Panelists evaluated samples for tenderness, connective tissue amount and juiciness. Sensory ratings were affected by formulation, anticipation of sample numbers and actual number of samples (sample x session). Although panelists discriminated among formulations presented in the last six samples somewhat differently from the way they discriminated among the same formulations presented in the first six samples, there was little evidence that the panel became less sensitive.     

APPLICATION OF FUZZY SETS AND NEURAL NETWORKS IN SENSORY ANALYSIS

This paper describes an initial but fundamental attempt to lay some groundwork for a fuzzy-set-based paradigm for sensory analysis and to demonstrate how fuzzy set and neural network techniques may lead to a natural way for sensory data interpretation. Sensory scales are described as fuzzy sets, sensory attributes as fuzzy variables, and sensory responses as sample membership grades. Multi-judge responses are formulated as a fuzzy membership vector or fuzzy histogram of response, which gives an overall panel response free of the unverifiable assumptions implied in conventional approaches. Neural networks are used to provide an effective tool for modeling and analysis of sensory responses in their naturally fuzzy and complex forms. A maximum method of defuzzification is proposed to give a crisp grade of the majority opinion. Two applications in meat quality evaluation are used to demonstrate the use of the paradigm and procedure. It is hoped that this work will bring up some new ideas and generate interest in research on application of fuzzy sets and neural networks in sensory analysis.     

厚颌鲂含肉率及生化成分的分析

研究了长江上游特有鱼类厚颌鲂的含肉率及其肌肉生化成分和能值。结果表明 :厚颌鲂的含肉率为72 72 % ;其肌肉生化成分 (鲜重百分比 ) :水分含量为 79 2 6%、蛋白质 16 82 %、脂肪 2 10 %、灰分 1 0 8%、无氮浸出物 0 73 %、比能值 4 93KJ/g及E/P2 9 3 3KJ/g。肌肉生化成分含量与体长变化无明显关系。经单因素方差分析 5— 6月份样品与 10月份样品在脂肪和无氮浸出物含量上有显著差异 (P <0 0 5)。肌肉中含有 17种氨基酸 ,总量为69 54% (干重百分比 ) ;9种人体必需氨基酸总量是 3 3 0 7% ,占氨基酸总量比例为 47 55% ;必需氨基酸指数 (EAAI)是 61 75。缬氨酸和异亮氨酸为限制性氨基酸 ;四种鲜味氨基酸含量为 5 85% (鲜重百分比 )。     

DEVELOPMENT OF A LEXICON FOR BEEF FLAVOR IN INTACT MUSCLE

ABSTRACT

A lexicon describing the flavor characteristics of beef across different cuts, grades, and cooking temperatures and methods was developed. Four major cuts of the United States Department of Agriculture (USDA) quality grade beef were cooked to five endpoint temperatures using braising, broiling (oven broiling and electric charbroiling), roasting and grilling (indoor and outdoor grilling). Six highly trained panelists identified and defined a total of 38 aroma and flavor characteristics in 176 beef samples. Beef identity, brown/roasted, bloody/serumy, metallic, fat‐like, overall sweet, sour aromatics and five tastes were present in practically all samples. Other attributes were present only in certain samples, depending on either the sample group or the cooking method/endpoint temperature combination used. This lexicon potentially offers the beef industry a standard tool to identify and quantify flavor attributes as impacted by temperature, cooking method, aging process, storage time, diet regime, packaging, USDA quality grades, etc.

PRACTICAL APPLICATIONS

Until now, the beef industry's main focus has been to assess beef tenderness and juiciness, with an emphasis on ways to improve beef texture. Meat companies and academic institutions have been using the 1995 American Meat Science Association guidelines to assess the flavor of beef, which are not comprehensive. Recent work has focused on flavor, and the industry needs a standardized flavor lexicon that can be used for many projects. It is important for the industry to be able to systematically identify and quantify flavor attributes that drive consumer acceptance.     

PHYSICAL AND SENSORY CHARACTERISTICS OF CAT LITTER1

Eighteen commercial cat litters were characterized for the following physical characteristics: water holding capacity, drying time, pH, cost, density, residual water, and particle size. Using sensory analysis, eight of the litters were evaluated for dustiness, clumpability and odor control before and after use and storage. The cat litters differed substantially in both physical and sensory properties. The crystal litters were noted for their small uniform size, high water holding capacity and clumpability. The recycled litter was most dusty and ammoniacal; whereas, the granule litter had an innately offensive odor. The alfalfa litter had superior odor control, but neither clumped nor dried well. The clay litters could be differentiated by pH, residual water, water holding capacity and could be classified as either perfumed and nonperfumed. Some of the perfumed litters became intensely perfumed when used and stored; whereas, the other perfumed litters behaved like the nonperfumed litters and became more offensive.     

USE OF COMPUTER-GENERATED IMAGES AND CONJOINT ANALYSIS TO INVESTIGATE SENSORY EXPECTATIONS

Consumers unfamiliar with passion-fruit juice were presented with 24 computer generated package images on which six packaging factors (background color, picture, information, brand, language and shape) had been manipulated and asked to give scores for six expected sensory attributes (sweetness, pureness, sharpness, refreshing, freshness, naturalness) and liking. Significant effects were obtained for each attribute, with background color and information being the most important. Consumers varying in Need For Cognition (NFC) were affected differently by the investigated features of the package. High NFC individuals used less of the packaging factors than low NFC subjects did.     

SOME ISSUES IN THE DESIGN AND ANALYSIS OF SENSORY DATA: REVISITED

Sensory experimental data whether collected via the consumer or via the laboratory population have many characteristics in common. Among the more important characteristics are that they are relative, skewed, and difficult to replicate. These characteristics result from the use of human beings as instruments to measure multivariate stimuli as opposed to the use of a mechanical instrument. The use of human beings as instruments presents several problems in the design and analysis of sensory/consumer studies. In the clinical/medical applications of sensory evaluation, we have used people both as the experimental unit (panelists/subjects) and as the instrument (judges) to measure panelists’ responses. Thus it is my belief that sensory data are the most difficult scientific data to statistically analyze and interpret. Unfortunately, limited theoretical work has been done to tackle the resulting problems. These problems include the comparison of responses obtained from independent populations (monadic design), sample size estimation with special reference to time-dependent responses, analysis of degree of difference data, the invariance property of rating scales, effect of correlated errors in observations, and the use of laboratory panels to predict results from a consumer panel. Some solutions to these problems have been provided by experience. My purpose here is to discuss each of these problems in greater detail and offer some possible scientific solutions.